Mobile devices, such as mobile phones, are more and more pervasive in many aspects of a user's daily lifestyle. However, in many respects a mobile device is still a dumb companion, with little or no awareness of its user's context, e.g. where they are, what they're doing, etc.
Mobile devices typically include one or more built-in sensors. The sensors are configured to take readings of the user's environment, such as the ambient air temperature, the device's geographic location, or the intensity of the ambient lighting. Whilst such readings may be used to infer a user's context, the sensors must generally be repeatedly polled or activated in order for the device to maintain a contextual awareness of the user. Therefore, a current solution to the above problem is to continuously monitor the context of the user using the sensors now integrated into modern smartphones, for example (accelerometer, digital compass, ambient light sensor, GPS positioning sensor, etc.), and/or explicitly requiring the user to enter their state via the handset (e.g. ‘checking in’ to a social media service such as Foursquare).
Repeatedly activating and taking readings from a mobile device's sensors, however, creates a noticeable drain on the device's power supply, such as its battery. This power drain is especially noticeable when the sensors are continuously active, when maintaining a constant contextual awareness. It is also burdensome and inconvenient for a user to continuously activate specific sensors of a device in order to take readings. Furthermore, users may not want their location information (for example) to be sent back to a mobile network before a user's context may be determined. Instead, users may be more content for the mobile device to determine their context internally without the need to transmit data to a third party.
There is therefore a need in the art to provide an improved mobile device that may provide services with contextual awareness, and thereby address the disadvantages encountered in the prior art.